EM-BASED POINT TO PLANE ICP FOR 3D SIMULTANEOUS LOCALIZATION AND MAPPING
Bibliographic record
Abstract
D simultaneous localization and mapping (SLAM) is a very impor- tant issue in autonomous robotics. One of the popular algorithms applied as a frontend of SLAM is iterative closest point (ICP). In this paper, the ICP is modelled into a probabilistic framework including both pose estimation and data association steps using expectation maximization (EM). The result derived is that the solu- tion converges to a local minimum if both pose estimation and data association steps employ the same metric. Hence, the measurement model which determines the form of the metric should be the key factor of the algorithm. Then, the point to point, point to plane and plane to plane are analysed in form of their measurement model, which reveals their description of the connection between two scans. Based on analysis, an improvement on point to plane measurement model is presented by estimating the covariance of each plane to relax the model assumption of ICP using eigenvalue decomposition, hence achieving a better solution. The following experiments show a satisfactory performance of the proposed algorithm, in agreement with the theoretic results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".